Our preliminary structure-based investigations show that water exclusion from deficiently packed hydrogen bonds and other pre-formed electrostatic interactions constitutes a driving factor conferring high specificity to protein association. Thus, an evolutionary conserved feature, the under-dehydrated hydrogen bond, termed dehydron, appears to be a structural marker for interactivity. Dehydrons were experimentally and statistically shown to constitute sticky spots on the protein surface and to be abundant at protein-protein interfaces, especially at those that cannot be understood in terms of standard interactions. The dehydron distribution on the surface of soluble proteins constitutes a determinant of the propensity for association and aberrant aggregation. The identification of dehydrons has relied so far on detailed structural information, a limitation precluding a proteomic analysis. This proposal is geared at introducing a sequence-based predictive method to establish the biological relevance of dehydrons and their potential as markers for drugable targets. Thus, we intend to introduce a powerful unsupervised scanning technology to detect signals of interactivity and drugability at a genomic scale. This goal requires constructing a machine-learning discriminator trained on a structural database. The over-all aim is to develop a sequence-based multi-purpose tool to expand the universe of drugable targets, diagnose propensity for aberrant aggregation and make interactomic inferences. The efficacy of our predictor will be tested on five grounds: a) Assaying for amino-acid variability and determining whether residues predicted solely from sequence to be engaged in dehydrons are actually conserved, b) Using a redundancy-free curated PDB sample as training set, we shall determine the accuracy and precision of the sequence-based predictor using a nonhomologous PDB complement set and annotated SwissProt entries as testing sets, c) Contrasting our results with an alternative dehydron predictor based on a reliable sequence-based predictor of native disorder (PONDR(r)). This dehydron predictor is based on a correlation found between the extent of hydrogen-bond packing and the score of structural disorder, d) Contrasting sequence-based diagnosis of amyloidogenic aggregation with SwissProt annotations and other annotated disease-related sequence repositories; e) Contrasting compiled drug-target quality assessments and structural data and screening profiles for protein-ligand associations with the predicted dehydron patterns. Thus, the novel design concept of "drug inhibitor as a wrapper of functional packing defects" will be explored and validated.